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A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination Type de document : Article/Communication Auteurs : Kaili Zhang, Auteur ; Yonggang Chen, Auteur ; Wentao Wang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2158948 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spatiale
[Termes IGN] analyse spectrale
[Termes IGN] classification Spectral angle mapper
[Termes IGN] classification spectrale
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données vectorielles
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] pixel
[Termes IGN] précision de la classification
[Termes IGN] signature texturale
[Termes IGN] similitude spectrale
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a worthwhile topic for improving classification accuracy. In this context, this study constructed a spectral-spatial feature, namely the Pixel Neighbourhood Similarity (PNS) index. Meanwhile, the PNS index and 19 spectral, textural and terrain features were involved in the Correlation-based Feature Selection (CFS) algorithm for feature selection to generate a feature combination (PNS-CFS). To explore how PNS and PNS-CFS improve the classification accuracy of land types. The results show that: (1) The PNS index exhibited clear boundaries between different land types. The performance quality of PNS was relatively highest compared to other spectral-spatial features, namely the Vector Similarity (VS) index, the Change Vector Intensity (CVI) index and the Correlation (COR) index. (2) The Overall Accuracy (OA) of the PNS-CFS was 94.66% and 93.59% in study areas 1 and 2, respectively. These were 7.48% and 6.02% higher than the original image data (ORI) and 7.27% and 2.39% higher than the single-dimensional feature combination (SIN-CFS). Compared to the feature combinations of VS, CVI, and COR indices (VS-CFS, CVI-COM, COR-COM), PNS-CFS had the relatively highest performance and classification accuracy. The study demonstrated that the PNS index and PNS-CFS have a high potential for image classification. Numéro de notice : A2023-059 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2158948 Date de publication en ligne : 03/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2158948 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102397
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2158948[article]
Titre : Mobile mapping mesh change detection and update Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Cédric Demonceaux, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Projets : PLaTINUM / Gouet-Brunet, Valérie Importance : 7 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maillage par triangles
[Termes IGN] mosaïquage d'images
[Termes IGN] semis de points
[Termes IGN] série temporelle
[Termes IGN] Stéréopolis
[Termes IGN] système de numérisation mobile
[Termes IGN] vision par ordinateurRésumé : (auteur) Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simu- lation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets. Numéro de notice : P2023-003 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2303.07182 Date de publication en ligne : 13/03/2023 En ligne : https://doi.org/10.48550/arXiv.2303.07182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102860 MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)
[article]
Titre : MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction Type de document : Article/Communication Auteurs : Du Yin, Auteur ; Renhe Jiang, Auteur ; Jiewen Deng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 77 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation temporelle dynamique (algorithme)
[Termes IGN] données multitemporelles
[Termes IGN] données spatiotemporelles
[Termes IGN] flux
[Termes IGN] gestion de trafic
[Termes IGN] origine - destination
[Termes IGN] réseau neuronal de graphes
[Termes IGN] système de transport intelligent
[Termes IGN] trafic urbain
[Termes IGN] transport public
[Termes IGN] utilisateurRésumé : (auteur) The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods. Numéro de notice : A2023-113 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-022-00466-1 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1007/s10707-022-00466-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102478
in Geoinformatica > vol 27 n° 1 (January 2023) . - pp 77 - 105[article]Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data Type de document : Article/Communication Auteurs : Hong Hu, Auteur ; Guanghe Zhang, Auteur ; Jianfeng Ao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2153929 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] image RVB
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle numérique de surface
[Termes IGN] Perceptron multicouche
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Airborne light detection and ranging (LiDAR) is a popular technology in remote sensing that can significantly improve the efficiency of digital elevation model (DEM) construction. However, it is challenging to identify the real terrain features in complex areas using LiDAR data. To solve this problem, this work proposes a multi-information fusion method based on PointNet++ to improve the accuracy of DEM construction. The RGB data and normalized coordinate information of the point cloud was added to increase the number of channels on the input side of the PointNet++ neural network, which can improve the accuracy of the classification during feature extraction. Low and high density point clouds obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and the United States Geological Survey (USGS) were used to test this proposed method. The results suggest that the proposed method improves the Kappa coefficient by 8.81% compared to PointNet++. The type I error was reduced by 2.13%, the type II error was reduced by 8.29%, and the total error was reduced by 2.52% compared to the conventional algorithm. Therefore, it is possible to conclude that the proposed method can obtain DEMs with higher accuracy. Numéro de notice : A2023-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2022.2153929 Date de publication en ligne : 23/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2153929 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102389
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2153929[article]
Titre : Open mapping towards sustainable development goals : Voices of youthmappers on community engaged scholarship Type de document : Monographie Auteurs : Patricia Solís, Éditeur scientifique ; Marcela Zeballos, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 382 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-031-05182-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Afrique occidentale
[Termes IGN] approche participative
[Termes IGN] Asie (géographie politique)
[Termes IGN] cartographe
[Termes IGN] cartographie thématique
[Termes IGN] catastrophe naturelle
[Termes IGN] changement climatique
[Termes IGN] développement durable
[Termes IGN] données localisées
[Termes IGN] eau
[Termes IGN] édition en libre accès
[Termes IGN] formation
[Termes IGN] géopolitique
[Termes IGN] OpenStreetMap
[Termes IGN] universitéRésumé : (éditeur) This collection amplifies the experiences of some of the world’s young people who are working to address SDGs using geospatial technologies and multi-national collaboration. Authors from every region of the world who have emerged as leaders in the YouthMappers movement share their perspectives and knowledge in an accessible and peer-friendly format. YouthMappers are university students who create and use open mapping for development and humanitarian purposes. Their work leverages digital innovations - both geospatial platforms and communications technologies - to answer the call for leadership to address sustainability challenges. The book conveys a sense of robust knowledge emerging from formal studies or informal academic experiences - in the first-person voices of students and recent graduates who are at the forefront of creating a new map of the world. YouthMappers use OpenStreetMap as the foundational sharing mechanism for creating data together. Authors impart the way they are learning about themselves, about each other, about the world. They are developing technology skills, and simultaneously teaching the rest of the world about the potential contributions of a highly connected generation of emerging world leaders for the SDGs. The book is timely, in that it captures a pivotal moment in the trajectory of the YouthMappers movement’s ability to share emerging expertise, and one that coincides with a pivotal moment in the geopolitical history of planet earth whose inhabitants need to hear from them. Most volumes that cover the topic of sustainability in terms of youth development are written by non-youth authors. Moreover, most are written by non-majoritarian, entrenched academic scholars. This book instead puts forward the diverse voices of students and recent graduates in countries where YouthMappers works, all over the world. Authors cover topics that range from water, agriculture, food, to waste, education, gender, climate action and disasters from their own eyes in working with data, mapping, and humanitarian action, often working across national boundaries and across continents. To inspire readers with their insights, the chapters are mapped to the United Nations 17 Sustainable Development Goals (SDGs) in ways that connect a youth agenda to a global agenda. With a preface written by Carrie Stokes, Chief Geographer and GeoCenter Director, United States Agency for International Development (USAID). This is an open access book. Note de contenu : 1- Introduction
Part I- Mapping for the goals on poverty, hunger, health, education, gender, water, and energy
2- Open data addressing challenges associated with informal settlements in the global South
3- Leveraging spatial technology for agricultural intensification to address hunger in Ghana
4- Rural household food insecurity and child malnutrition in Northern Ghana
5- Where is the closest health clinic? YouthMappers map their communities before and during the COVID-19 pandemic
6- Cross-continental youthmappers action to fight schistosomiasis transmission in Senegal
7- Understanding youthmappers’ contributions to building resilient communities in Asia
8- Activating education for sustainable development goals through youthmappers
9- Seeing the world through maps: An inclusive and youth-oriented approach
10- Youth engagement and the water–energy–land nexus in Costa Rica
11- Power grid mapping in West Africa
12- Mapping access to electricity in urban and rural Nigeria
Part II- Youth action on work, leadership, innovation, inequality, cities, production and land
13- Stories from students building sustainability through transfer of leadership
14- Drones for good: Mapping out the SDGs using innovative technology in Malawi
15- Assessing youthmappers contributions to the generation of open geospatial data in Africa
16- Mapping invisible and inaccessible areas of Brazilian cities to reduce inequalities
17- Visualizing youthMappers’ contributions to environmental resilience in Latin AmericaNuméro de notice : 24082 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-031-05182-1 En ligne : https://doi.org/10.1007/978-3-031-05182-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102333 Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)PermalinkPSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)PermalinkDes relevés sur mesure pour la sentinelle des Pyrénées / Marielle Mayo in Géomètre, n° 2209 (janvier 2023)PermalinkA survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)PermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)PermalinkTree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning / Stefano Puliti in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkTree position estimation from TLS data using hough transform and robust least-squares circle fitting / Maja Michałowska in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)PermalinkTree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkUAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) / Martin Štroner in European journal of remote sensing, vol 56 n° 1 (2023)PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)Permalink